There are a number of applications when imaging skin where one is looking for anatomical features or physiological responses independent of skin colour. The presence of epidermal melanin can mask these features or responses. For example, it is more difficult to detect non-blanchable erythema in darker skin patients [1]. Thus there is a motivation to determine the degree of light that is being absorbed and scattered by epidermal melanin and compensate for those losses of light. The state-of-the-art typically relies on an optical model of skin where the model partitions the losses of light between epidermal melanin and other skin constituents and structures [2].
Skin has a complex structure that varies depending on anatomical location, age, health, environmental exposure and ethnicity. Developing an omnibus optical model of skin that is able to capture the complexity and diversity of skin anatomy is a serious challenge. Typically skin is modelled as a multi-layered system with each layer being optically homogeneous [3]. The accuracy of the optical properties for the individual layers is questionable as they are usually derived from a limited set of reference samples often measured under conditions that do not represent the in-vivo state of skin. Based on these limited models, light propagation in skin can be approximated using Monte Carlo simulation methods [3] or solutions to the radiative transport equation [4, 5]. In practice the inverse problem needs to be solved. The inverse model uses the measured light losses from skin as inputs to the model that partitions the losses between melanin and the constituents of skin that are of interest. Solving the inverse problem with the appropriate optical model provides estimates of the presence and concentration of constituents in skin independent of the melanin concentration. However, the structure of the underlying skin and the optical properties of these structures in the in-vivo state need to be known and incorporated into the optical model in order for the method to accurately predict the propagation of light within the target tissue.